Prediction of pull-out behavior of timber glued-in glass fiber reinforced polymer and steel rods under various environmental conditions based on ANN and GEP models

Taleshi, Mostafa Mohammadzadeh; Tajik, Nima; Mahmoudian, Alireza; Yekrangnia, Mohammad

doi:10.1016/j.cscm.2023.e02842

Cited by 5 publications

(1 citation statement)

References 153 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if it is not, the chromosome will be reiterated by numerous processes such as mutation, replication, transposition, and recombination to create new ETs [39,67]. The same evaluation process is repeated until the best solutions or predefined fitness are achieved [68][69][70]. Figure 9a shows an example of chromosome structure in ET.…”

Section: Samples and Methodsmentioning

confidence: 99%

Fracture Density Prediction of Basement Metamorphic Rocks Using Gene Expression Programming

Hasan,

Tóth

2024

Minerals

View full text Add to dashboard Cite

Many methods have been developed to detect and predict the fracture properties of fractured rocks. The standard data sources for fracture evaluations are image logs and core samples. However, many wells do not have these data, especially for old wells. Furthermore, operating both methods can be costly, and, sometimes, the data gathered are of bad quality. Therefore, previous research attempted to evaluate fractures indirectly using the widely available conventional well-logs. Sedimentary rocks are widespread and have been studied in the literature. However, fractured reservoirs, like igneous and metamorphic rock bodies, may also be vital since they provide fluid migration pathways and can store some hydrocarbons. Hence, two fractured metamorphic rock bodies are studied in this study to evaluate any difference in fracture responses on well-log properties. Also, a quick and reliable prediction method is studied to predict fracture density (FD) in the case of the unavailability of image logs and core samples. Gene expression programming (GEP) was chosen for this study to predict FD, and ten conventional well-log data were used as input variables. The model produced by GEP was good, with R2 values at least above 0.84 for all studied wells, and the model was then applied to wells without image logs. Both selected metamorphic rocks showed similar results in which the significant parameters to predict FD were the spectral gamma ray, resistivity, and porosity logs. This study also proposed a validation method to ensure that the FD value predictions were consistent using discriminant function analysis. In conclusion, the GEP method is reliable and could be used for FD predictions for basement metamorphic rocks.

show abstract